Security Games for Node Localization through Verifiable

Multilateration

ABSTRACT:

Most applications of wireless sensor networks (WSNs) rely on data about the positions of sensor

nodes, which are notnecessarily known beforehand. Several localization approaches have been

proposed but most of them omit to consider that WSNscould be deployed in adversarial settings,

where hostile nodes under the control of an attacker coexist with faithful ones. Verifiable

multilateration (VM) was proposed to cope with this problem by leveraging on a set of trusted

landmark nodes that act as verifiers.Although VM is able to recognize reliable localization

measures, it allows for regions of undecided positions that can amount to the40 percent of the

monitored area. We studied the properties of VM as a noncooperative two-player game where

the first playeremploys a number of verifiers to do VM computations and the second player

controls a malicious node. The verifiers aim at securelylocalizing malicious nodes, while

malicious nodes strive to masquerade as unknown and to pretend false positions. Thanks to

gametheory, the potentialities of VM are analyzed with the aim of improving the defender’s

strategy. We found that the best placement forverifiers is an equilateral triangle with edge equal

to the power range R, and maximum deception in the undecided region isapproximately 0:27R.

Moreover, we characterized—in terms of the probability of choosing an unknown node to

examine further—thestrategies of the players.

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EXISTING SYSTEM:

At first, we study how the strategy of the malicious nodeat the equilibrium changes as

discretization grain jSjchanges and as the number of nonmalicious nodes changes.We searched

for a Nash equilibrium with jSj 2 ½10; 50 and a

step of 2 applied to the experimental setting described inthe previous section. Given that multiple

Nash equilibriacan coexist in a single game, each with different properties,we searched a specific

Nash equilibrium to have aconsistent comparison of the strategies. More precisely,we searched

for the Nash equilibrium minimizing theexpected utility of the malicious node by solving

themathematical programming problems described in Section5.2.2 with the objective function

min um. The basic model to prevent DoS attacks is a two-playergeneral-sum noncooperative

game between the attackernode and the WSNs . Given a fixed node i, theattacker’s available

actions are: attack sensor node i doesnot attack at all, or attack a different actor sensor

node;while the WSNs’ available actions are two: defend sensornode i, or defend a different

sensor node.

PROPOSED SYSTEM:

Verifiablemultilateration (VM) was proposed to cope with this proThus, several localization

schemes have been proposedbut most of thecurrent approaches omit to consider that WSNs

could bedeployed in adversarial settings, where hostile nodes underthe control of an attacker

coexist with faithful ones. Wirelesscommunications are easy to tamper, and nodes are prone

tophysical attacks and cloning; thus, classical solutions, basedon access control and strong

authentication, are difficult todeploy due to limited power resources of nodes. this direction, a

well-defined approach to localize nodeseven when some of them are compromised was proposed

inand it is known as verifiable multilateration (VM).

CONCLUSION:

In this paper, we studied a novel game theoretical scenariofor WSNs where verifiable

multilateration is employed toassess the presence of malicious nodes. We built a gametheoretical

framework where verifiers and malicious nodescompete one against each other as rational

players. First, westudied the best placement of the verifiers to minimizethe maximum deception

of the malicious node and wederived the equilibrium prescribing the optimal strategy forthe

verifiers and for the malicious node. We studied the casewith three verifiers and subsequently we

extended the resultto an arbitrary number of verifiers showing how, as thisnumber increases, the

maximum deception of the maliciousnode decreases. Second, we studied how the malicious

nodechanges its strategy when a number of nonmalicious nodesare present. We did this by

considering the best strategy forthe malicious node when verifiers can inspect one node. To find

the equilibrium, we provided a mixed-integer-linear programming formulation and we

experimentally showed that the Nash equilibria of the game almost everywhere coincide with the

malicious node’s maxminstrategyWe also aim at extending our framework to handle multiple

malicious nodes, additional security countermeasures, and energy constraints.

SYSTEM CONFIGURATION:-

HARDWARE CONFIGURATION:-

Processor - Pentium –IV

Speed - 1.1 Ghz

RAM - 256 MB(min)

Hard Disk - 20 GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

SOFTWARE CONFIGURATION:-

Operating System : Windows XP

Programming Language : JAVA

Java Version : JDK 1.6 & above.